29,054 research outputs found
Grid-Obstacle Representations with Connections to Staircase Guarding
In this paper, we study grid-obstacle representations of graphs where we
assign grid-points to vertices and define obstacles such that an edge exists if
and only if an -monotone grid path connects the two endpoints without
hitting an obstacle or another vertex. It was previously argued that all planar
graphs have a grid-obstacle representation in 2D, and all graphs have a
grid-obstacle representation in 3D. In this paper, we show that such
constructions are possible with significantly smaller grid-size than previously
achieved. Then we study the variant where vertices are not blocking, and show
that then grid-obstacle representations exist for bipartite graphs. The latter
has applications in so-called staircase guarding of orthogonal polygons; using
our grid-obstacle representations, we show that staircase guarding is
\textsc{NP}-hard in 2D.Comment: To appear in the proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
Graphs with Plane Outside-Obstacle Representations
An \emph{obstacle representation} of a graph consists of a set of polygonal
obstacles and a distinct point for each vertex such that two points see each
other if and only if the corresponding vertices are adjacent. Obstacle
representations are a recent generalization of classical polygon--vertex
visibility graphs, for which the characterization and recognition problems are
long-standing open questions.
In this paper, we study \emph{plane outside-obstacle representations}, where
all obstacles lie in the unbounded face of the representation and no two
visibility segments cross. We give a combinatorial characterization of the
biconnected graphs that admit such a representation. Based on this
characterization, we present a simple linear-time recognition algorithm for
these graphs. As a side result, we show that the plane vertex--polygon
visibility graphs are exactly the maximal outerplanar graphs and that every
chordal outerplanar graph has an outside-obstacle representation.Comment: 12 pages, 7 figure
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies.Comment: 20 page
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